Optical Coherence Tomography (OCT) is technique for imaging into samples, such as tissue, glass and the like. Recent advances in OCT include Fourier Domain (FD)-OCT, in which either a broadband source with a spectrometer (SD-OCT) or a swept laser source with a single photodiode (SS-OCT) is used to generate OCT images. These OCT architectures may be dependent on the dispersion properties of a reference arm and a sample arm, particularly the relative variation in dispersion between the two arms. This variation in dispersion can be compensated either by changing the optical properties of the sample or reference arm or by using numerical compensation techniques in the OCT processing and imaging software. This numerical compensation may require one or more parameters for the dispersion management.
Referring now to FIG. 1, a schematic block diagram illustrating a conventional technique for dispersion parameter optimization will be discussed. As illustrated in FIG. 1, the system includes and OCT imaging system 100 and Offline processing 101. As further illustrated in FIG. 1, the OCT imaging system 100 may be used for data acquisition, entering the parameters into the software, acquiring the image and displaying the image. The Offline Processor 101 may be used to analyze the data acquired by the OCT system 100 and generate/optimize the parameters entered into the OCT imaging system 100. Using conventional methods, the one or more parameters typically may be determined either by trial and error, for example, the system user may try various values until an optimal parameter value is determined. Alternatively, offline or post processing 101 associated with the OCT imaging system 100 may be used to search for the optimal parameter values. Conventional processes are discussed in, for example, ULTRAHIGH-RESOLUTION HIGH SPEED RETINAL IMAGING USING SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY by Teresa C. Chen (Optics Express, Volume 12, No. 11, May 31, 2004) and Ultrahigh-resolution, high-speed, FOURIER DOMAIN OPTICAL COHERENCE TOMOGRAPHY AND METHODS FOR DISPERSION COMPENSATION by Wojtkowski et al. (Optics Express, Volume 12, No. 11, May 31, 2004), the disclosures of which are incorporated herein by reference as if set forth in their entirety.
Two separate dispersion compensation algorithms are discussed in Wojtkowski. The first involves re-scaling of raw spectrum data during interpolation of SDOCT data from wavelength space to wavenumber space. This algorithm may provide fast results, since it does not involve complex computation, but the results may not be accurate, since it only corrects dispersion at a single depth in the sample.
According to the second algorithm discussed in Wojtkowski, the corrected spectrum data is obtained from a Hilbert transform of the raw spectrum modified by optimized phase correction parameters. This algorithm may provide accurate results, since it corrects dispersion for all depths in the sample simultaneously, but the results are not provided fast, since the Hilbert transform process as proposed is a computationally complex operation involving multiple forward and inverse Fourier transformations.
Thus, the processes discussed with respect to FIG. 1 and in the Chen and Wojtkowski references may be inaccurate (providing sub-optimal images), excessively time consuming (thus possibly preventing real time system operation), or may require detailed information regarding the specific dispersion properties of the system optics and/or each sample to be imaged. Accordingly, improved methods of numerical dispersion compensation may be desired.